Papers by Ahsan Wahab
A Large-Scale Study of Machine Translation in Turkic Languages (2021.emnlp-main)
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Jamshidbek Mirzakhalov, Anoop Babu, Duygu Ataman, Sherzod Kariev, Francis Tyers, Otabek Abduraufov, Mammad Hajili, Sardana Ivanova, Abror Khaytbaev, Antonio Laverghetta Jr., Bekhzodbek Moydinboyev, Esra Onal, Shaxnoza Pulatova, Ahsan Wahab, Orhan Firat, Sriram Chellappan
| Challenge: | a large corpus covering 22 Turkic languages is included in this paper . low-resource MT evaluation has traditionally focused on European languages due to limitations of available technology and resources. |
| Approach: | They present a case study of the practical application of MT in the Turkic language family . they propose to realize the gains of NMT for Turkic languages under high-resource to extremely low-resourced scenarios. |
| Outcome: | The proposed study shows that the new methods can be used in the Turkic language family . the results highlight bottlenecks in building competitive systems . |
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets (2022.tacl-1)
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Julia Kreutzer, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Ortiz Suarez, Iroro Orife, Kelechi Ogueji, Andre Niyongabo Rubungo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, Mofetoluwa Adeyemi
| Challenge: | Lower-resource corpora have systematic issues, including mislabeled or nonstandard/ambiguous language codes. |
| Approach: | They manually audit the quality of 205 language-specific corpora released with five major public datasets. |
| Outcome: | The results show that lower-resource corpora have systematic issues even for non-proficient speakers. |